Validating the accuracy of a model to predict the onset of myopia in children.

PURPOSE: To assess the sensitivity and specificity of models predicting myopia onset among ethnically Chinese children. METHODS: Visual acuity, height, weight, biometry (A-scan, keratometry), and refractive error were assessed at baseline and 3 years later using the same equipment and protocol in primary schools in Xiamen (China) and Singapore. A regression model predicting the onset of myopia < -0.75 diopters (D) after 3 years in either eye among Xiamen children was validated with Singapore data. RESULTS: Baseline data were collected from 236 Xiamen children (mean age, 7.82 ± 0.63 years) and from 1979 predominantly Chinese children in Singapore (7.83 ± 0.84 years). Singapore children were significantly taller and heavier, and had more myopia (31.4% vs. 6.36% < -0.75 D in either eye, P < 0.001) and longer mean axial length. Three-year follow-up was available for 80.0% of Xiamen children and 83.1% in Singapore. For Xiamen, the area under the receiver-operator curve (AUC) in a model including ocular biometry, height, weight, and presenting visual acuity was 0.974 (95% confidence interval [CI], 0.945-0.997). In Singapore, the same model achieved sensitivity, specificity, and positive predictive value of 0.844, 0.650, and 0.669, with an AUC of 0.815 (95% CI, 0.791-0.839). CONCLUSIONS: Accuracy in predicting myopia onset based on simple measurements may be sufficient to make targeted early intervention practical in settings such as Singapore with high myopia prevalence. Models based on cohorts with a greater prevalence of high myopia than that in Xiamen could be used to assess accuracy of models predicting more severe forms of myopia.

People and places: Understanding geographical accuracy in administrative data from the census and healthcare systems

Administrative systems such as health care registration are of increasing importance in providing information for statistical, research, and policy purposes. There is thus a pressing need to understand better the detailed relationship between population characteristics as recorded in such systems and conventional censuses. This paper explores these issues using the unique Northern Ireland Longitudinal Study (NILS). It takes the 2001 Census enumeration as a benchmark and analyses the social, demographic and spatial patterns of mismatch with the health register at individual level. Descriptive comparison is followed by multivariate and multilevel analyses which show that approximately 25% of individuals are reported to be in different addresses and that age, rurality, education, and housing type are all important factors. This level of mismatch appears to be maintained over time, as earlier migrants who update their address details are replaced by others who have not yet done so. In some cases, apparent mismatches seem likely to reflect complex multi-address living arrangements rather than data error.

Functionality, Accuracy, and Feasibility: Talking with Modelers

Models are an important part of many policy development processes, but meeting policy objectives relies on policy analysts engaging effectively with the modeling process and modelers understanding the policy issues. Furthermore, there are many different modeling methods, each with characteristics that potentially make it more or less suitable for analyzing a particular policy issue.
This paper presents a novel framework to assist policy analysts to engage with modelers so as to make the best use of models. The framework has three dimensions: Functionality, Accuracy and Feasibility. Functionality concerns ways in which modeling can be used to support broader policy objectives, such as promoting negotiation or comparing options. Accuracy concerns how to best represent the fundamental features of the system being modeled, and relies on selecting an appropriate technique. Feasibility concerns practical issues such as access to data and modeling skills.

STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies

Incomplete reporting has been identified as a major source of avoidable waste in biomedical research.
Essential information is often not provided in study reports, impeding the identification, critical
appraisal, and replication of studies. To improve the quality of reporting of diagnostic accuracy
studies, the Standards for Reporting Diagnostic Accuracy (STARD) statement was developed. Here
we present STARD 2015, an updated list of 30 essential items that should be included in every
report of a diagnostic accuracy study. This update incorporates recent evidence about sources of
bias and variability in diagnostic accuracy and is intended to facilitate the use of STARD. As such,
STARD 2015 may help to improve completeness and transparency in reporting of diagnostic accuracy
studies.